The Merit of High-Frequency Data in Portfolio Allocation

43 Pages Posted: 12 Sep 2011

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research

Lada M. Kyj

Humboldt University of Berlin; Quantitative Products Laboratory

Peter Malec

University of Cambridge - Faculty of Economics

Date Written: September 12, 2011

Abstract

This paper addresses the open debate about the effectiveness and practical relevance of high-frequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained over longer horizons than previous studies have shown. We propose a Multi-Scale Spectral Components model for forecasting high-dimensional covariance matrices based on realized measures employing HF data. Extensive performance evaluation confirms that the proposed approach dominates prevailing methods and validates the intuition that HF data used properly can translate into better portfolio allocation decisions.

Keywords: spectral decomposition, mixing frequencies, factor model, blocked realized kernel, covariance prediction, portfolio optimization

JEL Classification: G11, G17, C58, C14, C38

Suggested Citation

Hautsch, Nikolaus and Kyj, Lada M. and Malec, Peter, The Merit of High-Frequency Data in Portfolio Allocation (September 12, 2011). Available at SSRN: https://ssrn.com/abstract=1926098 or http://dx.doi.org/10.2139/ssrn.1926098

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Kolingasse 14
Vienna, A-1090
Austria

Lada M. Kyj

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK 10099
Germany

Quantitative Products Laboratory ( email )

Alexanderstrasse 5
Berlin, 10099
Germany

Peter Malec (Contact Author)

University of Cambridge - Faculty of Economics ( email )

Sidgwick Avenue
Cambridge, CB3 9DD
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
287
Abstract Views
2,584
Rank
198,305
PlumX Metrics